Friday, January 27, 2012

Madden 12 - What attributes effect OVR and the percentage they effect it by position

**  UPDATE - If you like this blog post, check out my new BLOG PAGE:
In this blog post you will see what attributes contribute to the overall (OVR) of each position in Madden 12, and what percentage (%) of those attributes contribute to OVR.  This post will have a lot of data. It's important to understand that there are several ways to get a player to 99 OVR, and that there are more than 99 OVR points available within these attributes.  Basically, the percentage will not apply equally to every player since some attributes can be lowered and others can be raised to come up with the same overall.  What this will tell you is which attributes carry the most weight when it comes to overall.  It will also show you which attributes to look for when you see an increase/decrease in OVR after a roster update.

    I got the idea to do this because as consumers we have never been told what the formula is for overall, and to completely understand Madden Ratings we need a better idea of how the OVR formula works.  I don't claim that this data is perfect, but I do think it paints a very good picture of the OVR formula.

How I did this:

1.  First I went into player edit mode to identify which attributes effect OVR at each position.

2.  Once the attributes where identify, I had to figure out a consistent way of computing the impact of those attributes to OVR.

3.  I started by lowering every attribute that had no effect on OVR to 0.  Then, I raised each attribute that did effect OVR to 99.

4.  While raising the attributes that mattered to 99, I realized that a 99 OVR was reached before all attributes were set to 99.  Because awareness (AWR) weighs heavy in OVR at all positions, I left every other attribute that mattered at 99 and lowered awareness until I reached 98 OVR.  By doing this, I could be sure of the total change in OVR by moving the other attributes individually.

5.  I would lower one attribute at a time to 0 and compute the change in overall.  Before moving on to the next attribute, I moved the previous one back up to 99.  Doing this helped me keep my procedures and calculations consistent.

6.  In the end I increased AWR to 99 while lowering other attributes until I reached 98 OVR, this allowed me to calculate the change in OVR when I moved AWR to 0.  Finally, I totaled up the change in OVR at each postion to get a total of the OVR points available.

7.  They percentages (%) you will see is the OVR point change for each attribute divided by the total OVR points available at that position.  This was my way of finding out which attributes changed the OVR rating the most.

**  Please know that this was not an exact science and at times the OVR points available to a position could be different by a few points.  Basically, you could do this for a position and come up with 172 OVR points available and I could come up with 174.  It's not a huge difference, but it's there.  I do believe that these numbers would be very close to the real figures if EA was to release them.

- I will do each position one at a time and list my observations about each position before moving on to the next position.

- It's very important to note, that if you don't see an attribute listed for a position it's because it didn't have any effect on overall (OVR) based on my findings.

- The first column is the attribute, the second is the change in OVR and the third column is the percent (%) of the attribute based on total OVR points available.

    I like the percentage distribution in general, but remember since there are 160 OVR points available certain attributes must be lower regardless of the players true ability just to get to below 99 OVR.  So to get an 80 OVR QB you must manipulate the top 6 attributes the most.  This manipulation is where we see inaccurate and inconsistent attributes throughout the game.  You could have two QBs with a career 60% completion percentage and 40% deep accuracy, but most likely they will be very different in accuracy attributes.  This is because one QB is viewed as average (75 OVR) and the other is viewed as good (85 OVR).  So, while the percentages look good, the OVR is not determined by 99 available points, it's determine by 160.  This is why I believe that OVR is broken, it forces the ratings staff to lower attributes despite the players true ability.  Once a player has reached 99 OVR, other attributes can continue to go up without a change in OVR.

 * THA is not an actually attribute in the player edit/creation mode, so it's probably just some combination of SAC, MAC, DAC, or it's manually put into the game by the ratings person.

     I like the distribution of the percentages with the exception of CAT, I think it should play a bigger role for a RB. We still have the same problem here as with QBs.  You will constantly fight the attributes to hold an OVR to a predetermined number that the ratings person is trying to reach.  Right now, player ratings are done backwards because there is already an OVR in mind before the attributes are rated (adjusted).  It should be the other way around, accurate and consistent attributes should come before OVR.

    Fullbacks had the most available OVR points.  I was surprised to see how low STR was on this list.

    TE's fill so many different roles, that they have the most attributes that actually effect OVR.  I think PBS/PBF should play a larger role, both at 2% seems very low.  So, good pass blocking TE's won't really receive much credit for that skill and poor pass blocking TEs won't be penalized.

    Since CAT plays such a large role in OVR, this is why we see inaccurate/inconsistent CAT attributes.  The CAT attribute should be a players actual ability to catch the ball,  but it is commonly used to hold down OVR.  By doing this, you will see more drops during Madden gameplay even though the real NFL player might be very sure handed.  RTE is also commonly used to hold down OVR.


    I decided to put LT and RT together so they are easier to compare.  Notice how much PBS plays into a LTs OVR.  Of course they are responsible for blocking the QBs blind side in most cases (right handed QBs only).  So basically the easiest way to lower a LT is by dropping PBS.  The RT has a nice balance when it comes to blocking ability, so it would force a ratings person to either guess or gather some statistics to properly support and increase/decrease. does have RBK and PBK grades, but I haven't seen any statistics or analysis that would help some grade the footwork traits (RBF/PBF).  By the way, RBK and PBK themselves had no effect on OVR, just the strength and footwork attributes effect OVR.

    I actually did LG and RG completely separate and the numbers came out exactly the same.  So run blocking leads the way for guards, which is not surprising.  Notice that the % of AWR for a guard is very close to that of tackles.  Offensive lineman in general have a higher percentage going to AWR than offensive skill positions like WR, TE and RB.

    Centers and QBs have the highest percentage going to AWR on offense at 20%.  I really like that, and it seems to be consistent with how the NFL depends on centers and QBs.  There is a nice distribution here in blocking attributes.
    While the percentage of distribution is good here, there are still several ways to rate this player and it will be a juggling act to keep him in the 75-80 OVR range.  Again, you will have to underrate some attributes just hold OVR down.

    Once again, I did RE and LE separately and got the same exact figures.  Prior to my calculations, I would have guessed that PUR would be higher for DE's.

    DTs are one of my biggest pet-peeves when it comes to Madden Ratings.  You will often find that BSH and TAK are the most inaccurate and inconsistent of all these attributes.  From my observations, they tend to be the target for any weekly decreases and increases.  There is a DT in Madden 12 who has a 79 TAK attribute, but made 56 tackles this season and missed zero.  If the TAK attribute is the ability to make a tackle, then this type of inaccuracy should never happen.

    Furthermore, when came out with their new Tackle Efficiency Stat they did not include DTs or NTs.  I contacted them to ask why and they stated because DTs/NTs very rarely miss tackles.  We have a position where players "very rarely" miss tackles, yet the TAK attribute is often underrated in Madden 12 to hold down OVR.

    Basically, ROLB and LOLB are the same.  This is what I was talking about in the beginning of this post, you will have a small difference in OVR pts available at times.  

    Here again, PUR doesn't play much a role in OVR.  PUR is also an attribute that is typically overrated in Madden 12 when it comes to OLBs as a result of having less impact to OVR.  

    Notice, for MLBs the PUR attribute has more impact on OVR.  Another thing to notice, was that while  CAT had a small impact for OLBs, it has no impact for MLBs.  

    From what I've seen, BSH has been a favorite attribute to decrease at MLB when the ratings folks want to get a lower OVR.  

    I was actually pretty shocked to see that only 11 attributes contributed to the CB overall rating.  One attribute that jumped out as missing, was catch (CAT).  I did this one several times, to make sure I wasn't missing something.  No matter how I did it, CAT no impact on a CBs overall (OVR).  Also, notice the large gap between MCV and ZCV.  That gap is why you will see many CBs with better ZCV than MCV.  What really gets me about how ZCV and MCV are rated, is that it doesn't seem be based on what type of coverage the player is actually good (bad) at.  You will see CBs who play primarily MCV defense on their NFL team, yet they seem to receive more increases in ZCV.  This makes no sense when they play more MCV and are better at it than ZCV.

    I thought the FS distribution came out pretty good.  Good FSs are very aware and the position tends to play a heavy amount of zone coverage.  CAT does play a small role here for FSs, after not playing any role for CBs.

   You see TAK shoot up the chart for SS, this make sense when you think of how many SS play in the box to help stop the run.  POW (Hit Power) comes in at 6% and it's the highest percent for any defensive player when it comes to POW.  

   Not a shock to see only 3 attributes contribute to the OVR of punters and kickers.  Notice the punter distribution is almost a perfect three way split, where more weight is giving to accuracy for the kicker.  I couldn't leave out the kickers after my last blog, but I think we see why some KAC attributes seem too low.  It's because of how much KAC effects OVR.  Once again, instead of accurate and consistent attributes first, the OVR is decided than the attributes are adjusted to attain that OVR.


- One note about the KR/PR rating.  It has no effect on OVR and is actually just a number that anyone can move up or down, and as far as I could tell it has no connection to other attributes. 

-  Remember, this is not 100% perfect.  I did my best with what we have available as consumers and owners of the Madden 12 game.  

- In future roster updates these attributes are the ones you should focus on to figure how the OVR actually changed.  Right down the attributes before and after you download the newest update so you can identify the changes.  This is how you find sneaky decreases or increases.

- Just because an attribute does not contribute to OVR, does not mean it won't contribute to gameplay.

   Ok guys, that is enough for now.  I will refer back to this information in future blogs as I break things down further.  Please leave any comments, suggestions, corrections, or anything else you can think of.  Have a great weekend.

 *To receive information on my future blog posts, follow me on twitter @mannmicj

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Monday, January 23, 2012

Position Spotlight - Kicker's KAC/KPW

    Ok, first I want to urge all of you to stay with me on this one.  There are a lot of numbers and tables, but over the course of this post I will try to make some sense of it for you.  I decided to do kickers because of a request I received on the forum.  I wanted to wait until the regular season was over to put this together.  Please note, that do to time constraints I couldn't do every kicker in the league.  I selected 18 kickers total, including:  the top 5 in kick accuracy (KAC) in Madden 12 (Gould, Kaeding, Bailey, Janikowski, Barth), two rookies, some high profile kickers, and lesser known kickers.  What you should find after going through this post is that the kickers are rated inconsistently in KAC and kick power (KPW).  The perception of a kicker and the reality based on statistics do not always paint the same picture.

    This spotlight is based on regular season stats only.  I understand that by leaving out playoff stats it leaves the door open for a little more debate.  That said, I think these numbers speak for themselves for the most part.  I have included field goal kicking accuracy numbers from 2011, 2011-2009, and career statistics for all 18 kickers.  I started out by just looking at KAC, but thought it would be interesting to include some stats that might indicate KPW.  Below are my tables with definitions for each stat:

All statistics were collected from and Stats LLC.

R = Rookie
* = Clutch trait (Only 3 NFL Kickers have the clutch trait in Madden 12)

KAC = Madden 12 kick accuracy
KPW = Madden 12 kick power

FGM = FGs made
FGA = FGs attempted
% = % of FGs made

Red = Highest
Green = Lowest

2011 Regular Season Only

Name Team KAC FGM FGA %
Barth TB 93 26 28 92.9%
Bironas* TEN 93 29 32 90.6%
Henery - R PHI 85 24 27 88.9%
Janikowski OAK 94 31 35 88.6%
Gould CHI 96 28 32 87.5%
Bailey - R DAL 95 32 37 86.5%
Crosby GB 87 24 28 85.7%
Vinatieri* IND 89 23 27 85.2%
Akers SF 90 44 52 84.6%
Rackers HOU 88 32 38 84.2%
Hauschka SEA 85 25 30 83.3%
Hanson DET 88 24 29 82.8%
Tynes NYG 87 19 24 79.2%
Longwell* MIN 93 22 28 78.6%
Cundiff BAL 84 28 37 75.7%
Brown STL 86 21 28 75.0%
Mare CAR 85 21 28 75.0%
Avg / % 89.2 453 540 83.9%

* Please note Kaeding missed 2011 with injury.

2009 - 2011
Name Team KAC FGM FGA % FGM FGA      %
Bironas* TEN 93 80 90 88.9%
Henery - R PHI 85 24 27 88.9%   68 7689%  College career
Rackers HOU 88 75 85 88.2%
Longwell* MIN 93 65 74 87.8%
Vinatieri* IND 89 56 64 87.5%
Kaeding SD 95 55 63 87.3%
Bailey - R DAL 95 32 37 86.5%   57 72 79%  College career
Janikowski OAK 94 90 105 85.7%
Gould CHI 96 77 90 85.6%
Akers SF 90 108 127 85.0%
Mare CAR 85 71 84 84.5%
Barth TB 93 63 75 84.0%
Tynes NYG 87 65 79 82.3%
Cundiff BAL 84 72 89 80.9%
Hanson DET 88 57 71 80.3%
Brown STL 86 73 91 80.2%
Hauschka SEA 85 40 50 80.0%
Crosby GB 87 73 92 79.3%
Avg / % 89.6 1176 1393 84.4%

Career Accuracy
Name Team KAC FGM FGA % FGM  FGA %
Henery - R PHI 85 24 27 88.9%   68 7689% College career
Kaeding SD 95 173 200 86.5%
Bailey - R DAL 95 32 37 86.5%   57     72 79% College career
Bironas* TEN 93 189 219 86.3%
Gould CHI 96 187 218 85.8%
Barth TB 93 73 87 83.9%
Longwell* MIN 93 361 434 83.2%
Vinatieri* IND 89 387 467 82.9%
Akers SF 90 338 411 82.2%
Hanson DET 88 463 565 81.9%
Mare CAR 85 350 431 81.2%
Tynes NYG 87 157 194 80.9%
Brown STL 86 220 272 80.9%
Rackers HOU 88 264 330 80.0%
Janikowski OAK 94 293 368 79.6%
Crosby GB 87 131 165 79.4%
Hauschka SEA 85 41 52 78.8%
Cundiff BAL 84 132 172 76.7%
Avg / % 89.6 3815 4649 82.1%

* Please note for Career, I highlighted Kaeding because Henery is only a rookie.


1.  How in the world does the rookie kicker Bailey have a higher KAC than Bironas.  While Bailey had a very good rookie season (10th in NFL for FG accuracy this year), I don't think that earns the right to have a better accuracy than Bironas.  Is this more Dallas Cowboy bias?

2.  Compare the two rookie kickers Bailey undrafted (95 KAC) and Henery drafted 4th round (85 KAC).  Henery had a better FG% this season (on fewer attempts) and was by far the better college kicker, so how is it possible that Bailey is rated 10 KAC points higher than Henery?  Notice that Henery was 88.9% this year and 89% in his college career, that is consistency.  Henery was the only kicker drafted in the 2011 draft and no kickers were drafted in the 2010 draft.  Neither draft position, college history, or 2011 NFL statistics have helped Henery in his KAC.

3.  Robbie Gould has the highest KAC in Madden 12 with 96, yet these statistics don't necessarily support that rating.  He is accurate, but should he be the best?  Bironas was better than Gould this year, the last three years, and slightly better in career %.  Keading was hurt this season, but has a better three year % and career % than Gould.  You will find out later that Gould is the most accurate in this group when it comes to field goals of 50 yards or more.  Is his deep kicking accuracy enough to earn the highest KAC in Madden, or does Gould get a boost because he kicks in the windy city of Chicago eight times a year?  To be far I looked at his home and road splits. For his career Gould is 83.6% at Soldier Field and 88.0% on the road.  That is very good on the road to say the least.

4.  After looking at all three tables, it doesn't appear that the KAC attribute is based on just this year, last three years or career numbers.  KAC is all over the place, with the exception of the worst kickers who seem to be rated pretty well.

5.  It also doesn't appear that the home (outdoors or doom) stadium comes into play with KAC ratings either.

6.  I think a very good argument could be made that Bironas should have the highest KAC in Madden based on this year, the last three years, and his career.  Bironas has be incredibly consistent.

7.  Another interesting thing to note, is the the combined FG % of these kickers this year was 83.9% and over the last three years was 84.4%.  Pretty consistent even though some kickers in the group fluctuated quite and bit in those time periods.

- Now we are going to take a look at career long (50 or more yards) FG accuracy:

50+ Kick Accuracy 
Name Team KPW KAC 50+FGM 50+FGA 50+% LONG
Gould CHI 92 96 11 15 73% 57
Bironas* TEN 96 93 21 29 72% 60
Brown STL 98 86 28 43 65% 58
Longwell* MIN 91 93 24 39 62% 55
Barth TB 91 93 6 10 60% 55
Kaeding SD 91 95 10 17 59% 57
Hanson DET 87 88 50 90 56% 56
Tynes NYG 89 87 10 18 56% 53
Janikowski OAK 99 94 36 66 55% 63
Rackers HOU 94 88 26 48 54% 57
Akers SF 93 90 22 41 54% 57
Bailey - R DAL 91 95 2 4 50% 51
Crosby GB 98 87 12 24 50% 58
Hauschka SEA 94 85 3 6 50% 54
Henery - R PHI 94 85 1 2 50% 51
Vinatieri* IND 90 89 12 25 48% 57
Mare CAR 93 85 19 43 44% 54
Cundiff BAL 97 84 5 19 26% 56
Avg / %             


Copyright © 2012 STATS LLC. All Rights Reserved.


1.  There is actually some consistency on this one.  Gould shines here, even though it's only 15 attempts and at the same time Cundiff is horrible at long FGs and is KAC supports that.

2.  The KAC attribute can't just be based on long FG %.  I suggest the EA Madden ratings team consider using similar stats for long FG accuracy to create a new kicker attribute called LKA (Long Kick Accuracy).  This LKA would come into play on all kicks 50 or more yards.  This way kickers get credit for their long kicking accuracy and those who are not as good would pay the price in LKA.

    When looking at long field goal kicking accuracy, I believe a 3 year or career statistic is best since most kickers don't attempt a large number of kicks over 50 yards in just one season.

3.  Should long kicking accuracy and statistics play a role in kick power?  I would say yes and no.  It can't be the only factor.  I don't think it's a coincidence that Janikowski has the highest kick power in this group as well as the longest FG made at 63 yards.  That said, I think these stats could potentially show inaccuracy in the KPW attribute.  Someone like Hanson from Detroit has a long history of hitting FGs of 50 yards or more, yet he only has an 87 KPW.  I think that is hard to justify at this point.  I will look deeper into that with my next table.

Now we will look at average kickoff distances.

KO Avg = Kickoff distance average for the season

2011 2010
Name Team KPW KO Avg KO Avg
Janikowski OAK 99 63.3 65.5
Brown STL 98 65.5 66.7
Crosby GB 98 65.0 61.6
Cundiff BAL 97 67.8 71.1
Bironas* TEN 96 64.0 66.7
Rackers HOU 94 62.8 66.2
Hauschka SEA 94 66.3 61.2
Henery - R PHI 94 65.0
Akers SF 93 66.4 64.7
Mare CAR 93 64.9 65.0
Gould CHI 92 64.2 64.5
Longwell* MIN 91 64.8 62.3
Barth TB 91 61.7 No KO's 11'
Kaeding SD 91 62.9 Injuried 11'
Bailey - R DAL 91 65.3
Vinatieri* IND 90 No KO's both
Tynes NYG 89 65.9 62.5
Hanson DET 87 66.3 61.6
Avg / % 93.2 65.2 64.3


1.  After going through these numbers, I found that some players improved their kickoff distance this year while others actually had a lower kickoff distance.  This was interesting to me, because I didn't know how moving the NFL kickoff position up 5 yards was going to impact these numbers.  You can see the group average improved (due to kickers like Hauschka +5.1 and Hanson + 4.7), but some kickers in the group decreased this year (like Rackers, Cundiff, and Janikowski)

2.  It's 75 yards from the new kickoff point at the 35 yard line and the back of the endzone.   This means that while some kicks go out of the endzone, this years average is just past the goal-line.  Last years average for these players would have put the starting point for the return on the 5.7 yard line, since the kickoff was from the 30.  Of course this isn't all of the NFL kickers, so this is not exact for the entire NFL.

3. I think average kickoff distance together with long field goal kicking stats can provide a good guide for rating KPW in Madden.  You can see that while Hanson was lower on this list last year when it came to kickoff distance, he improved dramatically this year.  Hanson was actually tied this season for 10th in the NFL for longest kickoff average with Hauschka at 66.3 yards.  Hauschka has received credit for that, but Hanson has not.

4.  While Cundiff can't hit the broadside of a barn when attempting a FG at 50 or more yards, he booms kickoffs.  This is why you can't just look at long field goal statistics to determine KPW.  This is also why I think an LKA (Long Kick Accuracy) attribute would be a very good addition to the game.  While Cundiff has the leg strength to attempt a long FG, his accuracy drops like a rock at that point.  If you subtracted Cundiff's FGM and FGA of 50 yards or longer, he would be an 83% FG kicker instead of his career 77%.  We have SAC, MAC, and DAC for QB's in the game, why not have LKA (Long Kick Accuracy) for Kickers.

5.  If you based Janikowski's kick power off of his average kickoff distance he would not have the highest KPW in the game at 99.  In Janikoski's case, maybe there should be a kickoff KPW and field goal KPW.  You can see that Janikowski was below average for this group in 2011 and was slightly above average in 2010.

6.  How should KPW be rated for player like Vinatieri and Barth who do not perform kickoffs any longer?  Should they automatically get a lower KPW?  That is a tough one.

    OK, that is all folks.  I know it's a lot of information.  I basically covered three things in this blog: overall FG kick accuracy, long FG kick accuracy, and average kickoff distance.  With all three of these statistics combined, I think Madden can have more consistent and true to life kicking attributes (KPW, KAC, LKA).  Take care and thanks for following my blogs.

*** Please feel free to let me know if you see a typo in the attributes or statistics, I will fix it immediately.  Thank you.

Stay tuned for more Madden 13 player rating projections.  Thank you for following my blog.  You can also follow me on twitter @mannmicj

Wednesday, January 18, 2012

How important is it to the Madden Community to have accurate and consistent player ratings/attributes?

How important is it to the Madden Community to have accurate and consistent player ratings/attributes?

    For the last couple of days, I have been trying to come up with an answer to this question.  I am actually starting to lean towards an answer of, "not very important."

    Here is my thinking:

1.  It seems that only a small percentage of the Madden Community is actively involved in the Madden Ratings Debate.

2.  Of the Madden fans that do contribute to the Madden ratings debate, most of them only focus on OVR. Very few of them actually suggest increases/decrease to attributes.  As long as the focus stays on the OVR rating, it leaves the door open for EA to continue putting out inaccurate and inconsistent attributes.

3.  The tourney/highly competitive Madden gamers tend to just use the highest rated teams year in and year out.  They don't have much loyalty to their favorite NFL team when it comes to picking who to use in Madden.  These players are concerned with ratings/attributes only to the extent of finding a team that gives them the best chance to win.

4.  The lack of loyalty to a gamers favorite NFL team, results in them not getting involved in the Madden ratings debate for that team.  Having more fans point out inaccuracies in the ratings/attributes for all teams, will result in a better game.

- Additional info (1/19/12 7:24am) About #4, I didn't mean that fans should decide player attributes.  I meant more input would mean more attention to other teams.  More attention may result in more accurate ratings for that team.  I understand that a lot of fans are Homers and should not rate their own team, but that doesn't mean their favorite team doesn't deserve the same attention as more popular teams.  Sorry I didn't make that clear.

5.  Only a few EA Game Changers appear to have a true passion for Madden ratings/attributes.  Don't get me wrong, the EA Game Changers are great, but it seems more of them are concerned or specialize in other aspects of the game.  I would argue that attributes impact gameplay as much as any other part of the game.  Having inaccurate attribute ratings, results in a poor representation of the actual NFL player's ability.

6.  Last, but not least.  I think some in the Madden Community have just given up on the entire player ratings system.  It's been so inconsistent and inaccurate for so long, they don't even bother getting into the debate anymore.  If you fall in this category, I strongly urge you to get involved again.

    So where does this leave us?  I challenge everyone reading this to get more involved.  Spread the word about how important you think this topic is.  Demand that EA put out a full roster update blog, that not only shows the OVR change, but also what attributes have changed as well.  Look deeper than the OVR rating of a player/s, look at the attributes.  Look at how those attributes match-up with other players at the same position.  I think you will be very surprised at the inconsistency and inaccuracy within those attributes.

    Just reading this blog or other blogs is not enough.  Let your voices be heard.  You can give your input at the following places:

#MaddenRatingsDebate hash tag or @Donny_Moore on Twitter

Facebook at

EA Madden Roster and Ratings Discussion

    I would recommend twitter or facebook over the EA forum, it's been said that Donny follows those more than the forums.  That's all for now and thank you for the support.

Tuesday, January 10, 2012

Attribute Spotlight - Madden 12 Middle Linebacker tackle (TAK) attribute.

    This is the second position in a series, were I will look at the tackle (TAK) attribute and consistency in Madden 12.  Prior to this post, I was creating my own Missed Tackle (%) using solo tackles and missed tackle stats gathered from  A little over a week ago released a new stat called Tackle Efficiency Rating.  This rating is defined by PFF to be "the number of attempted tackles per miss."  They are using both solo tackles and tackle assist when computing this rating, along with missed tackles.  

    For this blog I will refer to the Combined Tackle Efficiency rating for middle linebackers (MLB).  PFF also provides Tackle Efficiency for run plays only, as well as pass plays only.  I believe the combined rating is the best one when trying to determine which players are the best at performing the actual act of tackling.  

    Athough this is a new stat/rating, I have shown in my previous blog on Corner Back (CB) TAK attribute that missed tackles could have been used previously to come up with a more consistent and accurate TAK attribute.  That said.  With this new Tackle Efficiency Rating from PFF, it should be even easier for EA to provide accurate, consistent, and realistic tackle attributes in future Madden titles.

    Of course, we all must first agree that the TAK attribute is the ability of said player to perform the act of tackling.  We must be clear, Tackle Efficiency isn't about the total number of tackles a player makes.  Tackle Efficiency is about how well the player performs the act of tackling.  

    Today, I selected the top 10 Middle Linebackers (MLB) in Madden 12 (based on most current update) and  used the TAK attribute to sort them.  You will see a lot of familiar names on this list.  I than took the last 3 years of Tackle Efficiency Ratings and included those figures in a table to analyze.  This will give us a better idea of the current accuracy of the TAK attribute for MLBs.  I intentionally did not include total tackles.  All of these players have posted high tackle numbers in the past/present and I don't believe the number of tackles should decide how well a player tackles in Madden.  I also picked one MLB out of the top 10 for comparison, that is Paul Posluszny.  Posluszny keep showing up among the top players in Tackle Efficiency so I decided to include him out of curiosity.  

Here is the table:

TAK = Madden 12 tackle attribute as of most updated roster 1/10/11.   

TakEff = ProFootballFocus Combined Tackle Efficiency Rating (the number of attempted tackles per miss.)              ** Please note, the higher the number, the better the Tackle Efficiency.

Top 10 Middle LB's for TAK attribute in Madden 12
2011 2010 2009 3YR AVG
Name TAK TakEff TakEff TakEff TakEff
Fletcher 99 9.1 12.6 11.2 11.0
Beason 97 N/A IR 16.0 12.0 14.0
Lofton 96 11.2 9.9 10.5 10.5
Willis 96 36.5 13.4 30.3 26.7
Tulloch 96 12.4 15.2 9.9 12.5
Jackson 96 14.8 N/A IR 11.3 13.1
Laurinaitis 96 27.5 14.3 21.0 20.9
E.J. Henderson 96 10.5 11.7 14.0 12.1
Lewis 96 11.9 10.5 13.1 11.8
Urlacher 96 10.7 9.3 N/A IR 10.0
Posluszny 94 18.4 23.5 14.7 18.9 30.5 at OLB in 2010

2011 2010 2009 3YR AVG
Name TAK TakEff TakEff TakEff TakEff
Willis 96 36.5 13.4 30.3 26.7
Laurinaitis 96 27.5 14.3 21.0 20.9
Beason 97 N/A IR 16.0 12.0 14.0
Jackson 96 14.8 N/A IR 11.3 13.1
Tulloch 96 12.4 15.2 9.9 12.5
E.J. Henderson 96 10.5 11.7 14.0 12.1
Lewis 96 11.9 10.5 13.1 11.8
Fletcher 99 9.1 12.6 11.2 11.0
Lofton 96 11.2 9.9 10.5 10.5
Urlacher 96 10.7 9.3 N/A IR 10.0
Posluszny 94 18.4 23.5 14.7 18.9 30.5 at OLB in 2010
© 2010 All rights reserved.

    The first table is sorted by the best Tackle (TAK) attribute in Madden 12, the second table is sorted by the best three year average for Combined Tackle Efficiency.  I highlighted the highest in each column.


1.  Based on this group of MLBs, Patrick Willis has clearly been the best at performing the act of tackling, followed by James Laurinaitis.

2.  While London Fletcher continues to pile up the tackles year after year, his actual TakEff is toward the bottom of this list.  Maybe the number of tackles a player makes in a season is what EA has been using to determine the TAK attribute.  Remember, prior to this new stat, missed tackles were still being tracked and could easy have been utilized to come up with a number for Missed Tackle% or Tackle Efficiency.

3.  While all of these players have an elite TAK attribute in Madden, is it to much to ask for the best tacklers to get recognized as such.  Granted, they lumped several good tacklers together, but shouldn't Willis, Laurinaitis, and Posluszny have a better tackle attribute than Fletcher?

4.  If we use these same attribute numbers, but assign them based on the average Tackle Efficiency for the last three years,  it would look something like this:

Name TAK
Willis 99
Laurinaitis 97
Posluszny 96
Beason 96
Jackson 96
Tulloch 96
E.J. Henderson 96
Lewis 96
Fletcher 96
Lofton 96
Urlacher 94

If we used only the Tackle Efficiency numbers from this season, if would look like this:

Name TAK
Willis 99
Laurinaitis 97
Posluszny 96
Jackson 96
Tulloch 96
Lewis 96
Lofton 96
Urlacher 96
E.J. Henderson 96
Fletcher 96
Beason 94

Notice the top players in 2011 are also the top players in 3 years average.

5.  I prefer to look at things in 3 or 4 year blocks when applicable.  Obviously rookies and FAs with no history in the NFL need to be considered differently.  

6.  I would suggest leaving Beason at his 3 year average since he was injured, while moving Fletcher down the list.

7.  What do you do about role players and players who don't see much action?  First, I would use what data is available.  NFL stats, College stats, College analysis/Draft profile and anything else having to do with the player's ability to tackle.  Next, would be to discuss this attribute with a ratings team.  I mean a real ratings team, not just one person.  A group of objective individuals coming to a consensus on that player's attribute. Until further data/stats paint a clearer picture, this attribute rating will be assigned while considering similar players at the position with a similar NFL history.  

8.  While these selected players don't show drastic differences in the TAK attribute, once you enter the tackle efficiency rating to the table the inaccuracy is clear.  There are several lower rated MLBs in Madden 12, that actually tackle better than some players on this list.  I stuck with this group because it shows that even among the BIG names, there is a clear difference in Tackle Efficiency.  

9.  If the total number of tackles was to play a role in a Madden attribute, shouldn't it be pursuit (PUR)?  Let me know what you think.